Abstract
We developed a simulation model to illustrate and evaluate the potential effects of opioid-related policies and interventions at the local (e.g., community) level. In the United States, the opioid epidemic was declared a national public health emergency in 2017 because of extremely large numbers of opioid-related overdose deaths. Overprescription of addictive opioid-based painkillers could lead to physical dependence with subsequent dose increase. Some patients switch to heroin to support their drug habit. The use of high doses of prescription opioids, heroin especially in combination with a more powerful synthetic opioid, fentanyl, can sometimes lead to overdose, which can be lethal. A number of prevention and treatment policies have been proposed and some implemented to fight the epidemic. These policies include prescription drug monitoring programs (PDMP), reduced initial opioid dose distribution of naloxone to counter overdose, medication-assisted treatment of problem users, and tamper-proof pills to prevent noncompliant behavior. The model describes the dynamics of opioid prescription and use in an interconnected community of pain patients. The model simulates individual patients’ life trajectories with respect to the use of opioids under different policies. The model includes potential policies based on the overdose and mortality rates of prescription opioid users, the overdose and mortality rates of heroin users, and the number of patients who turn to illicit means to acquire their drugs. Simulation study results show strong effects of naloxone use, very marginal short-term effects of PDMP compliance, and few to no positive effects of tamper-resistant medications on non-child opioid use trajectories.
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Bobashev, G., Goree, S., Frank, J., Zule, W. (2018). Pain Town, an Agent-Based Model of Opioid Use Trajectories in a Small Community. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_31
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DOI: https://doi.org/10.1007/978-3-319-93372-6_31
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